OpenRaiser/Intern-Atlas huggingface dataset:
Rectangular Young tableaux with local decreases and the density method for uniform random generation (short version)
Venue: arXiv cs.DM 2018
Year: 2018

In this article, we consider a generalization of Young tableaux in which we allow some consecutive pairs of cells with decreasing labels. We show that this leads to a rich variety of combinatorial formulas, which suggest that these new objects could be related to deeper structures, similarly to the ubiquitous Young tableaux. Our methods rely on variants of hook-length type formulas, and also on a new efficient generic method (which we call the density method) which allows not only to generate constrained combinatorial objects, but also to enumerate them. We also investigate some repercussions of this method on the D-finiteness of the generating functions of combinatorial objects encoded by linear extension diagrams, and give a limit law result for the average number of local decreases.
Perspectives of Using Oscillators for Computing and Signal Processing
Venue: arXiv cs.ET 2018
Year: 2018

It is an intriguing concept to use oscillators as fundamental building blocks of electronic computers. The idea is not new, but is currently subject to intense research as a part of the quest for 'beyond Moore' electronic devices. In this paper we give an engineering-minded survey of oscillator-based computing architectures, with the goal of understanding their promise and limitations for next-generation computing. We will mostly discuss non-Boolean, neurally-inspired computing concepts and put the emphasis on hardware and on circuits where the oscillators are realized from emerging, nanoscale building blocks. Despite all the promise that oscillatory computing holds, existing literature gives very few clear-cut arguments about the possible benefits of using oscillators in place of other analog nonlinear circuit elements. In this survey we will argue for finding the rationale of using oscillatory building blocks and call for benchmarking studies that compare oscillatory computing circuits to level-based (analog) implementations.
Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
Venue: arXiv cs.MA 2018
Year: 2018

Several smart city services rely on users contribution, e.g., data, which can be costly for the users in terms of privacy. High costs lead to reduced user participation, which undermine the success of smart city technologies. This work develops a scenario-independent design principle, based on public good theory, for resource management in smart city applications, where provision of a service depends on contributors and free-riders, which benefit from the service without contributing own resources. Following this design principle, different classes of algorithms for resource management are evaluated with respect to human-centered measures, i.e., privacy, fairness and social welfare. Trade-offs that characterize algorithms are discussed across two smart city application scenarios. These results might help Smart City application designers to choose a suitable algorithm given a scenario-specific set of requirements, and users to choose a service based on an algorithm that matches their preferences.
How much does a word weigh? Weighting word embeddings for word sense induction
Venue: arXiv cs.CL 2018
Year: 2018

The paper describes our participation in the first shared task on word sense induction and disambiguation for the Russian language RUSSE'2018 (Panchenko et al., 2018). For each of several dozens of ambiguous words, the participants were asked to group text fragments containing it according to the senses of this word, which were not provided beforehand, therefore the "induction" part of the task. For instance, a word "bank" and a set of text fragments (also known as "contexts") in which this word occurs, e.g. "bank is a financial institution that accepts deposits" and "river bank is a slope beside a body of water" were given. A participant was asked to cluster such contexts in the unknown in advance number of clusters corresponding to, in this case, the "company" and the "area" senses of the word "bank". The organizers proposed three evaluation datasets of varying complexity and text genres based respectively on texts of Wikipedia, Web pages, and a dictionary of the Russian language. We present two experiments: a positive and a negative one, based respectively on clustering of contexts represented as a weighted average of word embeddings and on machine translation using two state-of-the-art production neural machine translation systems. Our team showed the second best result on two datasets and the third best result on the remaining one dataset among 18 participating teams. We managed to substantially outperform competitive state-of-the-art baselines from the previous years based on sense embeddings.
A Unified Framework for Training Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

The lack of mathematical tractability of Deep Neural Networks (DNNs) has hindered progress towards having a unified convergence analysis of training algorithms, in the general setting. We propose a unified optimization framework for training different types of DNNs, and establish its convergence for arbitrary loss, activation, and regularization functions, assumed to be smooth. We show that framework generalizes well-known first- and second-order training methods, and thus allows us to show the convergence of these methods for various DNN architectures and learning tasks, as a special case of our approach. We discuss some of its applications in training various DNN architectures (e.g., feed-forward, convolutional, linear networks), to regression and classification tasks.
Cache-Aware QoE-Traffic Optimization in Mobile Edge Assisted Adaptive Video Streaming
Venue: arXiv cs.NI 2018
Year: 2018

Multi-access edge computing (MEC) enables placing video content at the edge of the network aiming to improve the quality of experience (QoE) of the mobile clients. Video content caching at edge servers also reduces traffic in the backhaul of the mobile network, hence reducing operational costs for mobile network operators (MNOs). However, minimizing the rate of cache misses and maximizing the average video quality may sometimes be at odds with each other, particularly when the cache size is constrained. Our objective in this article is two fold: First, we explore the impact of fixed video content caching on the optimal QoE of mobile clients in a setup where servers at mobile network edge handle bitrate selection. Second, we want to investigate the effect of cache replacement on QoE-traffic trade-off. An integer nonlinear programming (INLP) optimization model is formulated for the problem of jointly maximizing the QoE, the fairness as well as minimizing overall data traffic on the origin video server. Due to its NP-Hardness, we then present a low complexity greedy-based algorithm with minimum need for parameter tuning which can be easily deployed. We show through simulations that the joint optimization indeed enables striking a desired trade-off between traffic reduction and QoE. The results also reveal that with fixed cached contents, the impact of caching on the QoE is proportional to the desired operational point of MNO. Furthermore, the effect of cache replacement on QoE is less noticeable compared to its effect on backhaul traffic when cache size is constrained.
Online shortest paths with confidence intervals for routing in a time varying random network
Venue: arXiv cs.DS 2018
Year: 2018

The increase in the world's population and rising standards of living is leading to an ever-increasing number of vehicles on the roads, and with it ever-increasing difficulties in traffic management. This traffic management in transport networks can be clearly optimized by using information and communication technologies referred as Intelligent Transport Systems (ITS). This management problem is usually reformulated as finding the shortest path in a time varying random graph. In this article, an online shortest path computation using stochastic gradient descent is proposed. This routing algorithm for ITS traffic management is based on the online Frank-Wolfe approach. Our improvement enables to find a confidence interval for the shortest path, by using the stochastic gradient algorithm for approximate Bayesian inference.
Reinforcement Learning for Heterogeneous Teams with PALO Bounds
Venue: arXiv cs.LG 2018
Year: 2018

We introduce reinforcement learning for heterogeneous teams in which rewards for an agent are additively factored into local costs, stimuli unique to each agent, and global rewards, those shared by all agents in the domain. Motivating domains include coordination of varied robotic platforms, which incur different costs for the same action, but share an overall goal. We present two templates for learning in this setting with factored rewards: a generalization of Perkins' Monte Carlo exploring starts for POMDPs to canonical MPOMDPs, with a single policy mapping joint observations of all agents to joint actions (MCES-MP); and another with each agent individually mapping joint observations to their own action (MCES-FMP). We use probably approximately local optimal (PALO) bounds to analyze sample complexity, instantiating these templates to PALO learning. We promote sample efficiency by including a policy space pruning technique, and evaluate the approaches on three domains of heterogeneous agents demonstrating that MCES-FMP yields improved policies in less samples compared to MCES-MP and a previous benchmark.
WisenetMD: Motion Detection Using Dynamic Background Region Analysis
Venue: arXiv cs.CV 2018
Year: 2018

Motion detection algorithms that can be applied to surveillance cameras such as CCTV (Closed Circuit Television) have been studied extensively. Motion detection algorithm is mostly based on background subtraction. One main issue in this technique is that false positives of dynamic backgrounds such as wind shaking trees and flowing rivers might occur. In this paper, we proposed a method to search for dynamic background region by analyzing the video and removing false positives by re-checking false positives. The proposed method was evaluated based on CDnet 2012/2014 dataset obtained at "changedetection.net" site. We also compared its processing speed with other algorithms.
Predictive Local Smoothness for Stochastic Gradient Methods
Venue: arXiv cs.LG 2018
Year: 2018

Stochastic gradient methods are dominant in nonconvex optimization especially for deep models but have low asymptotical convergence due to the fixed smoothness. To address this problem, we propose a simple yet effective method for improving stochastic gradient methods named predictive local smoothness (PLS). First, we create a convergence condition to build a learning rate which varies adaptively with local smoothness. Second, the local smoothness can be predicted by the latest gradients. Third, we use the adaptive learning rate to update the stochastic gradients for exploring linear convergence rates. By applying the PLS method, we implement new variants of three popular algorithms: PLS-stochastic gradient descent (PLS-SGD), PLS-accelerated SGD (PLS-AccSGD), and PLS-AMSGrad. Moreover, we provide much simpler proofs to ensure their linear convergence. Empirical results show that the variants have better performance gains than the popular algorithms, such as, faster convergence and alleviating explosion and vanish of gradients.
Adaptive Stochastic Gradient Langevin Dynamics: Taming Convergence and Saddle Point Escape Time
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we propose a new adaptive stochastic gradient Langevin dynamics (ASGLD) algorithmic framework and its two specialized versions, namely adaptive stochastic gradient (ASG) and adaptive gradient Langevin dynamics(AGLD), for non-convex optimization problems. All proposed algorithms can escape from saddle points with at most $O(\log d)$ iterations, which is nearly dimension-free. Further, we show that ASGLD and ASG converge to a local minimum with at most $O(\log d/ε^4)$ iterations. Also, ASGLD with full gradients or ASGLD with a slowly linearly increasing batch size converge to a local minimum with iterations bounded by $O(\log d/ε^2)$, which outperforms existing first-order methods.
MAVI: A Research Platform for Telepresence and Teleoperation
Venue: arXiv cs.RO 2018
Year: 2018

One of the goals in telepresence is to be able to perform daily tasks remotely. A key requirement for this is a robust and reliable mobile robotic platform. Ideally, such a platform should support 360-degree stereoscopic vision and semi-autonomous telemanipulation ability. In this technical report, we present our latest work on designing the telepresence mobile robot platform called MAVI. MAVI is a low-cost and robust but extendable platform for research and educational purpose, especially for machine vision and human interaction in telepresence setups. The MAVI platform offers a balance between modularity, capabilities, accessibility, cost and an open source software framework. With a range of different sensors such as Inertial Measurement Unit (IMU), 360-degree laser rangefinder, ultrasonic proximity sensors, and force sensors along with smart actuation in omnidirectional holonomic locomotion, high load cylindrical manipulator, and actuated stereoscopic Pan-Tilt-Roll Unit (PTRU), not only MAVI can provide the basic feedbacks from its surroundings, but also can interact within the remote environment in multiple ways. The software architecture of MAVI is based on the Robot Operating System (ROS) which allows for the easy integration of the state-of-the-art software packages.
VisualBackProp for learning using privileged information with CNNs
Venue: arXiv cs.CV 2018
Year: 2018

In many machine learning applications, from medical diagnostics to autonomous driving, the availability of prior knowledge can be used to improve the predictive performance of learning algorithms and incorporate `physical,' `domain knowledge,' or `common sense' concepts into training of machine learning systems as well as verify constraints/properties of the systems. We explore the learning using privileged information paradigm and show how to incorporate the privileged information, such as segmentation mask available along with the classification label of each example, into the training stage of convolutional neural networks. This is done by augmenting the CNN model with an architectural component that effectively focuses model's attention on the desired region of the input image during the training process and that is transparent to the network's label prediction mechanism at testing. This component effectively corresponds to the visualization strategy for identifying the parts of the input, often referred to as visualization mask, that most contribute to the prediction, yet uses this strategy in reverse to the classical setting in order to enforce the desired visualization mask instead. We verify our proposed algorithms through exhaustive experiments on benchmark ImageNet and PASCAL VOC data sets and achieve improvements in the performance of $2.4\%$ and $2.7\%$ over standard single-supervision model training. Finally, we confirm the effectiveness of our approach on skin lesion classification problem.
Why developers cannot embed privacy into software systems? An empirical investigation
Venue: arXiv cs.SE 2018
Year: 2018

Pervasive use of software applications continues to challenge user privacy when users interact with software systems. Even though privacy practices such as Privacy by Design (PbD), have clear in- structions for software developers to embed privacy into software designs, those practices are yet to become a common practice among software developers. The difficulty of developing privacy preserv- ing software systems highlights the importance of investigating software developers and the problems they face when they are asked to embed privacy into application designs. Software devel- opers are the community who can put practices such as PbD into action. Therefore, identifying problems they face when embed- ding privacy into software applications and providing solutions to those problems are important to enable the development of privacy preserving software systems. This study investigates 36 software developers in a software design task with instructions to embed privacy in order to identify the problems they face. We derive rec- ommendation guidelines to address the problems to enable the development of privacy preserving software systems.
VisemeNet: Audio-Driven Animator-Centric Speech Animation
Venue: arXiv cs.GR 2018
Year: 2018

We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio. Our three-stage Long Short-Term Memory (LSTM) network architecture is motivated by psycho-linguistic insights: segmenting speech audio into a stream of phonetic-groups is sufficient for viseme construction; speech styles like mumbling or shouting are strongly co-related to the motion of facial landmarks; and animator style is encoded in viseme motion curve profiles. Our contribution is an automatic real-time lip-synchronization from audio solution that integrates seamlessly into existing animation pipelines. We evaluate our results by: cross-validation to ground-truth data; animator critique and edits; visual comparison to recent deep-learning lip-synchronization solutions; and showing our approach to be resilient to diversity in speaker and language.
FastFCA-AS: Joint Diagonalization Based Acceleration of Full-Rank Spatial Covariance Analysis for Separating Any Number of Sources
Venue: arXiv cs.SD 2018
Year: 2018

Here we propose FastFCA-AS, an accelerated algorithm for Full-rank spatial Covariance Analysis (FCA), which is a robust audio source separation method proposed by Duong et al. ["Under-determined reverberant audio source separation using a full-rank spatial covariance model," IEEE Trans. ASLP, vol. 18, no. 7, pp. 1830-1840, Sept. 2010]. In the conventional FCA, matrix inversion and matrix multiplication are required at each time-frequency point in each iteration of an iterative parameter estimation algorithm. This causes a heavy computational load, thereby rendering the FCA infeasible in many applications. To overcome this drawback, we take a joint diagonalization approach, whereby matrix inversion and matrix multiplication are reduced to mere inversion and multiplication of diagonal entries. This makes the FastFCA-AS significantly faster than the FCA and even applicable to observed data of long duration or a situation with restricted computational resources. Although we have already proposed another acceleration of the FCA for two sources, the proposed FastFCA-AS is applicable to an arbitrary number of sources. In an experiment with three sources and three microphones, the FastFCA-AS was over 420 times faster than the FCA with a slightly better source separation performance.
Multi-Scale DenseNet-Based Electricity Theft Detection
Venue: arXiv cs.CV 2018
Year: 2018

Electricity theft detection issue has drawn lots of attention during last decades. Timely identification of the electricity theft in the power system is crucial for the safety and availability of the system. Although sustainable efforts have been made, the detection task remains challenging and falls short of accuracy and efficiency, especially with the increase of the data size. Recently, convolutional neural network-based methods have achieved better performance in comparison with traditional methods, which employ handcrafted features and shallow-architecture classifiers. In this paper, we present a novel approach for automatic detection by using a multi-scale dense connected convolution neural network (multi-scale DenseNet) in order to capture the long-term and short-term periodic features within the sequential data. We compare the proposed approaches with the classical algorithms, and the experimental results demonstrate that the multiscale DenseNet approach can significantly improve the accuracy of the detection. Moreover, our method is scalable, enabling larger data processing while no handcrafted feature engineering is needed.
Crowd-Labeling Fashion Reviews with Quality Control
Venue: arXiv cs.CL 2018
Year: 2018

We present a new methodology for high-quality labeling in the fashion domain with crowd workers instead of experts. We focus on the Aspect-Based Sentiment Analysis task. Our methods filter out inaccurate input from crowd workers but we preserve different worker labeling to capture the inherent high variability of the opinions. We demonstrate the quality of labeled data based on Facebook's FastText framework as a baseline.
Duadic negacyclic codes over a finite non-chain ring and their Gray images
Venue: arXiv cs.IT 2018
Year: 2018

Let $f(u)$ be a polynomial of degree $m, m \geq 2,$ which splits into distinct linear factors over a finite field $\mathbb{F}_{q}$. Let $\mathcal{R}=\mathbb{F}_{q}[u]/\langle f(u)\rangle$ be a finite non-chain ring. In an earlier paper, we studied duadic and triadic codes over $\mathcal{R}$ and their Gray images. Here, we study duadic negacyclic codes of Type I and Type II over the ring $\mathcal{R}$, their extensions and their Gray images. As a consequence some self-dual, isodual, self-orthogonal and complementary dual(LCD) codes over $\mathbb{F}_q$ are constructed. Some examples are also given to illustrate this.
Efficient Encoding of Dynamical Systems through Local Approximations
Venue: arXiv cs.SY 2018
Year: 2018

An efficient representation of observed data has many benefits in various domains of engineering and science. Representing static data sets, such as images, is a living branch in machine learning and eases downstream tasks, such as classification, regression, or decision making. However, the representation of dynamical systems has received less attention. In this work, we develop a method to represent a dynamical system efficiently as a combination of a state and a local model, which fulfills a criterion inspired by the minimum description length (MDL) principle. The MDL principle is used in machine learning and statistics to quantify the trade-off between the ability to explain seen data and the model complexity. Networked control systems are a prominent example, where such a representation is beneficial. When many agents share a network, information exchange is costly and should thus happen only when necessary. We empirically show the efficiency of the proposed encoding for several dynamical systems and demonstrate reduced communication for event-triggered state estimation problems.